Cluster analysis of wireless channel impulse responses with hidden Markov models

This paper introduces a novel wireless channel clustering technique, based on the Saleh-Valenzuela channel model. The channel impulse response is regarded as a realization of the probabilistic channel model, based on which the prior density functions of cluster arrival times are derived. Cluster analysis is done by means of extending the Saleh-Valenzuela model to a non-stationary case and re-interpreting it in terms of mixture models. The parameters of the mixture are then learned with hidden Markov models. Once trained, the HMM could be used to optimally cluster the channel taps with the Viterbi algorithm. The proposed method has been applied to simulated as well as measured channel impulse responses and showed reasonably good performance.